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Neural Processing Letters
Erschienen in: Neural Processing Letters 6/2022

09.05.2022

Finite Time Stability of Caputo–Katugampola Fractional Order Time Delay Projection Neural Networks

verfasst von: Mengxue Dai, Yirong Jiang, Jinsheng Du, Guoji Tang

Erschienen in: Neural Processing Letters | Ausgabe 6/2022

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Abstract

This paper explores the finite time stability of Caputo–Katugampola fractional order projection neural network with time delay. By employing the Sadovskii fixed point theorem, the Banach fixed point theorem and the generalized Gronwall inequality, we establish the existence and boundedness theorems of solutions for Caputo–Katugampola fractional order time delay projected neural networks. Further, we apply the proposed theorems and the techniques of inequalities to obtain the finite time stability of the equilibrium point for the presented system. Finally, the effectiveness of the theoretical result is shown through simulations for a numerical example.
Vorheriger Artikel Leader-Following Consensus of Fractional-Order Uncertain Multi-Agent Systems with Time Delays
Nächster Artikel Passivity-based Bipartite Synchronization of Coupled Delayed Inertial Neural Networks via Non-reduced Order Method

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Metadaten
Titel
Finite Time Stability of Caputo–Katugampola Fractional Order Time Delay Projection Neural Networks
verfasst von
Mengxue Dai
Yirong Jiang
Jinsheng Du
Guoji Tang
Publikationsdatum
09.05.2022
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 6/2022
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-022-10838-1

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